Camera Model Identification With Unknown Models

Huang, Yonggang; Zhang, Jun; Huang, Heyan

doi:10.1109/tifs.2015.2474836

Cited by 31 publications

(15 citation statements)

References 33 publications

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“…On the other hand, such a good performance degrades sharply in the presence of a misalignment between training and test set: a system trained on original images will perform poorly on JPEG compressed images. This raises serious doubts on the practical applicability of the whole approach in real-world operations calling for new strategies to handle real-world and open set scenarios dealing with unknown models [1,13,26].…”

Section: Discussionmentioning

confidence: 99%

A study of co-occurrence based local features for camera model identification

Marra

Poggi

Sansone

et al. 2016

Multimed Tools Appl

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Camera model identification has great relevance for many forensic applications, and is receiving growing attention in the literature. Virtually all techniques rely on the traces left in the image by the long sequence of in-camera processes which are specific of each model. They differ in the prior assumptions, if any, and in how such evidence is gathered in expressive features. In this work we study a class of blind features, based on the analysis of the image residuals of all color bands. They are extracted locally, based on co-occurrence matrices of selected neighbors, and then used to train a classifier. A number of experiments are carried out on the well-known Dresden Image Database. Besides the full-knowledge case, where all models of interest are known in advance, other scenarios with more limited knowledge and partially corrupted images are also investigated. Experimental results show these features to provide a state-of-the-art performance

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Section: Discussionmentioning

confidence: 99%

A study of co-occurrence based local features for camera model identification

Marra

Poggi

Sansone

et al. 2016

Multimed Tools Appl

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show abstract

“…Therefore an optimal K should be chosen to balance the accuracy and size. For this parameter optimization is also introduced [1]. Algorithm for unknown detection is shown below:…”

Section: B Unknown Detectionmentioning

confidence: 99%

“…The remainder of the paper is structured as follows: related works are described in section II, section III presents a detailed description of the proposed system. Section IV reports the experimental results followed by conclusion in section V. Kai san choi and et al [1] believed that source camera can be identified by measuring the amount of lens aberration (ie, barrel or pincushion distortion) exhibited by each camera model. Sensor pattern noise is considered as an intrinsic fingerprint of each camera, thus SPN is commonly used for source camera identification.…”

Section: Introductionmentioning

confidence: 99%

Source Camera Identification using Feature Extraction

Anujan¹

2016

IJERT

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The means and incentive to create digital image forgeries increased because of the increasing use of digital image, thus feature based source camera identification plays a crucial role in the authentication of digital images. The drawback of the conventional systems is the problem of unknown models. To rectify the disadvantage, camera model identification with unknown model was introduced but the accuracy level was found 28%. To increase the accuracy to an acceptable level, the proposed system was introduced. The new scheme consist of four stages: 1) feature extraction 2) unknown detection 3) unknown expansion 4) classification. In feature extraction, the input image is represented in 10 different formats and from each format 34 features are extracted, thus a total of 340 features are extracted from a single image. Then a KNN based unknown detection and a self training based unknown expansion is done. Finally classification is done using multi-class SVM with quadratic kernel. The experiments were carried out on Dresden image collection which confirms the effectiveness of the proposed system. The accuracy of the proposed system is found to be about 86%

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“…However, the vast majority of images captured today do not contain digital watermarks. In the feature based approach, the features are firstly extracted from the images. Then, the identification is converted to a classification problem.…”

Section: Introductionmentioning

confidence: 99%

Anti‐noise image source identification

Liu

Huang

Zhang

et al. 2018

Concurrency and Computation

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Summary Image source identification plays an important role in image forensics. Traditional image source identification techniques rely on the assumption that the training datasets are well labeled. However, in the real world, this assumption may not be the truth, and some noisy samples may exist in the training datasets. In this paper, firstly, we theoretically investigate the influence of noisy samples to the identification performance. Then, a new image source identification approach, namely, Anti‐noise Image Source Identification (AISI), is proposed to deal with those noisy samples. AISI has three steps, ie, noise level evaluation, noise level based sampling, and multi‐classification. Noise level evaluation aims to assign each sample with a noise level that indicates the probability of being noisy. The basic idea of noise level based sampling is to sample images according to their noise levels. We provide a theoretically justification to demonstrate the effectiveness of AISI. Experiments conducted on a real‐world image collection confirm that the proposed AISI can alleviate the influence of noisy samples and can improve the identification accuracy while noise exists.

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Camera Model Identification With Unknown Models

Cited by 31 publications

References 33 publications

A study of co-occurrence based local features for camera model identification

A study of co-occurrence based local features for camera model identification

Source Camera Identification using Feature Extraction

Anti‐noise image source identification

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